Teach Large Language Models the Concept of Meta-cognition to Reduce Hallucination Text Generation

Xiang Li

Primary Area: generative models

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Keywords: large language models, meta-learning, low rank adapter, ai hallucination, prompt engineering

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TL;DR: In this paper, we construct an algorithm to train a language model to evaluate itself's ability to answer certain questions.

Abstract: We introduce an algorithm that endows language models with enduring meta-cognitive capabilities. Inspired by meta-learning, our approach involves fine-tuning models on diverse datasets, including the original base model. Throughout each training iteration, we randomly select various fine-tuned model versions, gauge their meta-cognitive capacities, and employ the meta-cognitive error average as the loss function for gradient updates. This empowers these models to assess their competence when interpreting human instructions, thereby averting the generation of responses beyond their abilities and mitigating hallucinatory text production. The meta-cognitive ability will be adapt to various fine-tuned versions of the main model, providing evaluations that align with the fine-tuned models' knowledge capacity.

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Submission Number: 2625